Ningxi Yang | Searcharxiv

arXiv2026

Multiband-Induced Complex Magnetotransport in the Kagome Metal LuNb$_6$Sn$_6$

We report the electrical transport properties of the kagome metal LuNb$_6$Sn$_6$, which undergoes a charge density wave (CDW) transition at $T_\mathrm{CDW}\approx80$ K. Similar to the kagome metals $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs), LuNb$_6$Sn$_6$ develops complex magnetotransport behavior near and below the CDW transition. In particular, the longitudinal magnetoconductivity exhibits an approximately linear field dependence below about 1 T, whereas the field dependence of Hall conductivity displays pronounced nonlinearity and a field-induced sign reversal. Although an anomalous Hall-like contribution can be extracted by subtracting a local background from the Hall conductivity, mobility-spectrum analysis shows that the overall magnetotransport can be largely accounted for by multiband conduction involving high-mobility electronlike and holelike carriers. Nevertheless, sizable deviations from the multiband model emerge near $T_{\mathrm{CDW}}$, pointing to additional CDW-related scattering processes. These results demonstrate that high-mobility multiband transport, together with CDW-induced modifications of carrier scattering, play important roles in the complex magnetotransport of LuNb$_6$Sn$_6$.

cond-mat.str-el↗

arXiv2025

QUAR-VLA: Vision-Language-Action Model for Quadruped Robots

The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentalize perception, planning, and decision-making, simplifying system design but limiting the synergy between different information streams. This compartmentalization poses challenges in achieving seamless autonomous reasoning, decision-making, and action execution. To address these limitations, a novel paradigm, named Vision-Language-Action tasks for QUAdruped Robots (QUAR-VLA), has been introduced in this paper. This approach tightly integrates visual information and instructions to generate executable actions, effectively merging perception, planning, and decision-making. The central idea is to elevate the overall intelligence of the robot. Within this framework, a notable challenge lies in aligning fine-grained instructions with visual perception information. This emphasizes the complexity involved in ensuring that the robot accurately interprets and acts upon detailed instructions in harmony with its visual observations. Consequently, we propose QUAdruped Robotic Transformer (QUART), a family of VLA models to integrate visual information and instructions from diverse modalities as input and generates executable actions for real-world robots and present QUAdruped Robot Dataset (QUARD), a large-scale multi-task dataset including navigation, complex terrain locomotion, and whole-body manipulation tasks for training QUART models. Our extensive evaluation (4000 evaluation trials) shows that our approach leads to performant robotic policies and enables QUART to obtain a range of emergent capabilities.

cs.RO↗